We present a weighted-LASSO method to infer the parameters of a first-order vector auto-regressive model that describes time course expression data generated by directed gene-to-gene regulation networks. These networks are assumed to own prior internal structures of connectivity which drive the inference method. This prior structure can be either derived from prior biological knowledge or infer...

We analyze the financial planning problems of young households whose main decisions are how to finance the purchase of a house (liabilities) and how to allocate investments in pension savings schemes (assets). The problems are solved using a multi–stage stochastic programming model where the uncertainty is described by a scenario tree generated from a vector auto-regressive process for equity r...

In this paper, a new nonlinear wavelet identification structure is proposed for high noise resistive soft sensors. This method uses proposedPolynomial Nonlinear Auto Regressive Exogenous Model, which can be solved with linear Gaussian Least Square Method, alongside the Averaging Wavelet Method (AWM) filter. AWM uses the approximation spaces for analyzing the signals and reduce the noise by a me...

This research investigates the effects of government expenditure in Uganda on infrastructure promoting sustainable economic. The study used a longitudinal design using financial records from years 1984-85 to 2015-16 as population with sample size 32 annual observations. Johansen cointegration test indicates long-run association between infrastructure, communication, electricity, and development...

Base metal prices, especially steel, play a significant role in industrial economics, making them worth knowing about future values. In most cases, we expect superior performance from multivariate forecasting models comparing univariate methods due to the involvement of explanatory variables system. Standard vector auto regressive model can only capture short-run dynamics because differencing p...

Efficient and robust fault detection and diagnosis (FDD) can potentially play an important role in developing building management systems (BMS) for high performance buildings. Our research indicates that, in comparison to traditional model-based or data-driven methods, the combination of time series modeling and machine learning techniques produces higher accuracy and lower false alarm rates in...

Safety is the most important to the mobile robots that coexist with human. There are many studies that investigate obstacle detection and collision avoidance by predicting obstacles’ trajectories several seconds into the future using mounted sensors such as cameras and laser range finder (LRF) for the safe behavior control of robots. In environments such as crossing roads where blind areas occu...

In response to questions about the relative importance of different types capital flow for international competitiveness, we develop a structural vector auto-regressive model real exchange rate and flows. We reveal that innovations speculative sentiment cause changes in competitiveness. report speculation replaces effect equity, bond most interest effect. The results show is an important contri...

This paper present a texture compression technique for still images based on the wavelet transform and the auto-regressive (AR) texture model in order to increase the compression ratio with a minimal loss of image quality. First the influences of the initial condition and the order of an AR model on the resulting texture model are investigated to serve as a theoretical foundation for the propos...

Parametric identification approaches in the Linear Parameter-Varying (LPV) setting require optimal prior selection of a set of functional dependencies, used in the parametrization of the model coefficients, to provide accurate model estimates of the underlying system. Consequently, data-driven estimation of these functional dependencies has a paramount importance, especially when very limited a...